SHF: Small: Taming Huge Page Problems for Memory Bulk Operations Using a Hardware/Software Co-Design Approach

SHF：小：使用硬件/软件协同设计方法解决内存批量操作的大页面问题

• 批准号: 2400014
• 负责人: Jun Wang
• 金额: $ 40万
• 依托单位: The University of Central Florida Board of Trustees
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2400014&HistoricalAwards=false
• 关键词: SHF; Small; Taming; Huge; Page

Recent advances in non-volatile memory (NVM) technologies promise significantly higher capacities compared to traditional dynamic random-access memory (DRAM). Today’s computers often combine NVM with DRAM to construct a unified main memory architecture that meets the extremely high demand of many emerging big data and artificial intelligence applications. However, managing ultra-high capacity memories introduces daunting systems challenges. In particular, the massive parallelism of NVM drives adoption of huge two megabytes or even larger memory pages. The successful completion of this project's activity will be a significant step towards accelerating the secure non-volatile memory architectures by orders of magnitude performance improvement. This project will also contribute to society through engaging under-represented groups from a Hispanic Serving Institution and research dissemination for computer science and engineering education and training.The research project tackles these issues using a hardware and software co-design methodology. In this project, a general-purpose, fine­-grained memory management system will be designed, modeled, and implemented to significantly improve both the speed and bandwidth of single-level, DRAM combined with ultra-low latency, non-volatile memory architecture for both central processing unit (CPU) and graphic processing unit (GPU) machines, in particular for huge page configurations. The unused and overprovisioned parts of existing hardware architecture units will be exploited via both non-intrusive and intrusive ways to enable fine-granularity memory management, and achieve high efficiency and effectiveness of the Copy-on-Write mechanism. Several new Copy-on-Write use cases, including the checkpointing and snapshot, and large language model serving will be further studied. Furthermore, the new memory architecture will be integrated with the high-speed processing workflow of big data management, artificial intelligence programs, and hardware security machines.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

与传统的动态随机记忆（DRAM）相比，非易失性记忆（NVM）技术的最新进展有望更高。当今的计算机通常将NVM与DRAM相结合，以构建统一的主要存储架构，该统一的主要记忆体系结构满足了许多新兴的大数据和人工智能应用程序的极高需求。但是，管理超高容量记忆会引入艰巨的系统挑战。特别是，NVM的巨大并行性驱动了大型两个兆字节甚至更大的记忆页。该项目活动的成功完成将是通过改进级数量级的订单来加速安全的非易失性内存体系结构的重要一步。该项目还将通过与西班牙裔服务机构和计算机科学和工程教育和培训的研究传播中的代表性不足的群体参与不足的团体为社会做出贡献。该研究项目使用硬件和软件共同设计方法解决了这些问题。在这个项目中，将设计，建模和实施通用，细粒度的内存管理系统，以显着提高单层，DRAM的速度和带宽，并结合了中央处理单元（CPU）和图形处理单元（GPU）机器（GPU）机器（尤其是大型页面配置）的超低延迟，非挥发性存储架构。现有硬件架构单元的未使用和过度配置的部分将通过非侵入性和侵入性的方法来探索，以实现精细粒度内存管理，并实现复制在写入机制的高效率和有效性。几个新的抄写用例，包括检查点和快照以及大型语言模型服务，将进一步研究。此外，新的内存体系结构将与大数据管理，人工智能程序和硬件安全机的高速处理工作流程集成。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响审查标准来通过评估来获得的支持。